Overview

Dataset statistics

Number of variables25
Number of observations699
Missing cells987
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory127.1 KiB
Average record size in memory186.2 B

Variable types

Categorical9
Numeric6
Text7
Boolean2
Unsupported1

Alerts

season has constant value ""Constant
country has constant value ""Constant
match_id is highly overall correlated with match_name and 4 other fieldsHigh correlation
runs is highly overall correlated with ballsFaced and 2 other fieldsHigh correlation
ballsFaced is highly overall correlated with runs and 2 other fieldsHigh correlation
fours is highly overall correlated with runs and 1 other fieldsHigh correlation
sixes is highly overall correlated with runs and 1 other fieldsHigh correlation
runningOver is highly overall correlated with isNotOutHigh correlation
match_name is highly overall correlated with match_id and 5 other fieldsHigh correlation
home_team is highly overall correlated with match_id and 5 other fieldsHigh correlation
away_team is highly overall correlated with match_id and 5 other fieldsHigh correlation
venue is highly overall correlated with match_id and 4 other fieldsHigh correlation
city is highly overall correlated with match_id and 4 other fieldsHigh correlation
current_innings is highly overall correlated with match_name and 2 other fieldsHigh correlation
isNotOut is highly overall correlated with runningOverHigh correlation
runningOver has 144 (20.6%) missing valuesMissing
commentary has 144 (20.6%) missing valuesMissing
link has 699 (100.0%) missing valuesMissing
link is an unsupported type, check if it needs cleaning or further analysisUnsupported
runs has 82 (11.7%) zerosZeros
ballsFaced has 11 (1.6%) zerosZeros
fours has 338 (48.4%) zerosZeros
sixes has 502 (71.8%) zerosZeros

Reproduction

Analysis started2023-11-03 16:18:17.379309
Analysis finished2023-11-03 16:18:25.638670
Duration8.26 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

season
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2022
699 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2796
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 699
100.0%

Length

2023-11-03T22:18:25.766538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-03T22:18:25.906435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2022 699
100.0%

Most occurring characters

ValueCountFrequency (%)
2 2097
75.0%
0 699
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2796
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2097
75.0%
0 699
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2796
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2097
75.0%
0 699
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2796
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2097
75.0%
0 699
 
25.0%

match_id
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1298156.7
Minimum1298135
Maximum1298179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-11-03T22:18:26.021778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1298135
5-th percentile1298136
Q11298144
median1298157
Q31298169
95-th percentile1298177
Maximum1298179
Range44
Interquartile range (IQR)25

Descriptive statistics

Standard deviation13.412498
Coefficient of variation (CV)1.0331956 × 10-5
Kurtosis-1.3207586
Mean1298156.7
Median Absolute Deviation (MAD)12
Skewness-0.021732866
Sum9.0741154 × 108
Variance179.89509
MonotonicityDecreasing
2023-11-03T22:18:26.173548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1298170 21
 
3.0%
1298151 21
 
3.0%
1298140 21
 
3.0%
1298161 20
 
2.9%
1298142 20
 
2.9%
1298158 19
 
2.7%
1298143 19
 
2.7%
1298135 19
 
2.7%
1298172 19
 
2.7%
1298138 19
 
2.7%
Other values (32) 501
71.7%
ValueCountFrequency (%)
1298135 19
2.7%
1298136 19
2.7%
1298137 18
2.6%
1298138 19
2.7%
1298139 15
2.1%
1298140 21
3.0%
1298141 12
1.7%
1298142 20
2.9%
1298143 19
2.7%
1298144 15
2.1%
ValueCountFrequency (%)
1298179 17
2.4%
1298178 9
1.3%
1298177 11
1.6%
1298176 18
2.6%
1298175 17
2.4%
1298174 16
2.3%
1298173 17
2.4%
1298172 19
2.7%
1298171 19
2.7%
1298170 21
3.0%

match_name
Categorical

HIGH CORRELATION 

Distinct42
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
PAK v SA
 
21
SL v UAE
 
21
BAN v NED
 
21
NZ v SL
 
20
WI v ZIM
 
20
Other values (37)
596 

Length

Max length11
Median length10
Mean length8.8354793
Min length7

Characters and Unicode

Total characters6176
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENG v PAK
2nd rowENG v PAK
3rd rowENG v PAK
4th rowENG v PAK
5th rowENG v PAK

Common Values

ValueCountFrequency (%)
PAK v SA 21
 
3.0%
SL v UAE 21
 
3.0%
BAN v NED 21
 
3.0%
NZ v SL 20
 
2.9%
WI v ZIM 20
 
2.9%
PAK v ZIM 19
 
2.7%
NED v SL 19
 
2.7%
NAM v SL 19
 
2.7%
AUS v AFG 19
 
2.7%
IRE v ZIM 19
 
2.7%
Other values (32) 501
71.7%

Length

2023-11-03T22:18:26.330653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
v 699
33.3%
ned 140
 
6.7%
sl 137
 
6.5%
zim 135
 
6.4%
pak 120
 
5.7%
ire 109
 
5.2%
eng 95
 
4.5%
india 94
 
4.5%
ban 90
 
4.3%
nz 82
 
3.9%
Other values (7) 396
18.9%

Most occurring characters

ValueCountFrequency (%)
1398
22.6%
v 699
11.3%
A 607
9.8%
N 550
 
8.9%
I 480
 
7.8%
E 399
 
6.5%
S 329
 
5.3%
D 234
 
3.8%
Z 217
 
3.5%
M 184
 
3.0%
Other values (12) 1079
17.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4079
66.0%
Space Separator 1398
 
22.6%
Lowercase Letter 699
 
11.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 607
14.9%
N 550
13.5%
I 480
11.8%
E 399
9.8%
S 329
8.1%
D 234
 
5.7%
Z 217
 
5.3%
M 184
 
4.5%
G 147
 
3.6%
L 137
 
3.4%
Other values (10) 795
19.5%
Space Separator
ValueCountFrequency (%)
1398
100.0%
Lowercase Letter
ValueCountFrequency (%)
v 699
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4778
77.4%
Common 1398
 
22.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
v 699
14.6%
A 607
12.7%
N 550
11.5%
I 480
10.0%
E 399
8.4%
S 329
 
6.9%
D 234
 
4.9%
Z 217
 
4.5%
M 184
 
3.9%
G 147
 
3.1%
Other values (11) 932
19.5%
Common
ValueCountFrequency (%)
1398
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1398
22.6%
v 699
11.3%
A 607
9.8%
N 550
 
8.9%
I 480
 
7.8%
E 399
 
6.5%
S 329
 
5.3%
D 234
 
3.8%
Z 217
 
3.5%
M 184
 
3.0%
Other values (12) 1079
17.5%

home_team
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
INDIA
76 
SL
70 
NZ
66 
ZIM
64 
PAK
56 
Other values (11)
367 

Length

Max length5
Median length3
Mean length3.018598
Min length2

Characters and Unicode

Total characters2110
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPAK
2nd rowPAK
3rd rowPAK
4th rowPAK
5th rowPAK

Common Values

ValueCountFrequency (%)
INDIA 76
10.9%
SL 70
10.0%
NZ 66
9.4%
ZIM 64
 
9.2%
PAK 56
 
8.0%
BAN 56
 
8.0%
SCOT 45
 
6.4%
AUS 37
 
5.3%
UAE 34
 
4.9%
NAM 34
 
4.9%
Other values (6) 161
23.0%

Length

2023-11-03T22:18:26.487941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
india 76
10.9%
sl 70
10.0%
nz 66
9.4%
zim 64
 
9.2%
pak 56
 
8.0%
ban 56
 
8.0%
scot 45
 
6.4%
aus 37
 
5.3%
uae 34
 
4.9%
nam 34
 
4.9%
Other values (6) 161
23.0%

Most occurring characters

ValueCountFrequency (%)
A 344
16.3%
N 281
13.3%
I 277
13.1%
S 170
 
8.1%
Z 130
 
6.2%
E 114
 
5.4%
D 109
 
5.2%
M 98
 
4.6%
U 71
 
3.4%
L 70
 
3.3%
Other values (10) 446
21.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2110
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 344
16.3%
N 281
13.3%
I 277
13.1%
S 170
 
8.1%
Z 130
 
6.2%
E 114
 
5.4%
D 109
 
5.2%
M 98
 
4.6%
U 71
 
3.4%
L 70
 
3.3%
Other values (10) 446
21.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2110
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 344
16.3%
N 281
13.3%
I 277
13.1%
S 170
 
8.1%
Z 130
 
6.2%
E 114
 
5.4%
D 109
 
5.2%
M 98
 
4.6%
U 71
 
3.4%
L 70
 
3.3%
Other values (10) 446
21.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 344
16.3%
N 281
13.3%
I 277
13.1%
S 170
 
8.1%
Z 130
 
6.2%
E 114
 
5.4%
D 109
 
5.2%
M 98
 
4.6%
U 71
 
3.4%
L 70
 
3.3%
Other values (10) 446
21.1%

away_team
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
NED
107 
ENG
79 
IRE
78 
ZIM
71 
SL
67 
Other values (10)
297 

Length

Max length5
Median length3
Mean length2.8168813
Min length2

Characters and Unicode

Total characters1969
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowENG
2nd rowENG
3rd rowENG
4th rowENG
5th rowENG

Common Values

ValueCountFrequency (%)
NED 107
15.3%
ENG 79
11.3%
IRE 78
11.2%
ZIM 71
10.2%
SL 67
9.6%
PAK 64
9.2%
SA 63
9.0%
BAN 34
 
4.9%
AUS 29
 
4.1%
UAE 21
 
3.0%
Other values (5) 86
12.3%

Length

2023-11-03T22:18:26.661002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ned 107
15.3%
eng 79
11.3%
ire 78
11.2%
zim 71
10.2%
sl 67
9.6%
pak 64
9.2%
sa 63
9.0%
ban 34
 
4.9%
aus 29
 
4.1%
uae 21
 
3.0%
Other values (5) 86
12.3%

Most occurring characters

ValueCountFrequency (%)
E 285
14.5%
N 269
13.7%
A 263
13.4%
I 203
10.3%
S 159
8.1%
D 125
 
6.3%
G 98
 
5.0%
Z 87
 
4.4%
M 86
 
4.4%
R 78
 
4.0%
Other values (7) 316
16.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1969
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 285
14.5%
N 269
13.7%
A 263
13.4%
I 203
10.3%
S 159
8.1%
D 125
 
6.3%
G 98
 
5.0%
Z 87
 
4.4%
M 86
 
4.4%
R 78
 
4.0%
Other values (7) 316
16.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1969
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 285
14.5%
N 269
13.7%
A 263
13.4%
I 203
10.3%
S 159
8.1%
D 125
 
6.3%
G 98
 
5.0%
Z 87
 
4.4%
M 86
 
4.4%
R 78
 
4.0%
Other values (7) 316
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1969
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 285
14.5%
N 269
13.7%
A 263
13.4%
I 203
10.3%
S 159
8.1%
D 125
 
6.3%
G 98
 
5.0%
Z 87
 
4.4%
M 86
 
4.4%
R 78
 
4.0%
Other values (7) 316
16.0%

venue
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Bellerive Oval, Hobart
136 
Sydney Cricket Ground
118 
Adelaide Oval
114 
Simonds Stadium, South Geelong, Victoria
108 
Perth Stadium
85 
Other values (2)
138 

Length

Max length48
Median length24
Mean length24.745351
Min length13

Characters and Unicode

Total characters17297
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMelbourne Cricket Ground
2nd rowMelbourne Cricket Ground
3rd rowMelbourne Cricket Ground
4th rowMelbourne Cricket Ground
5th rowMelbourne Cricket Ground

Common Values

ValueCountFrequency (%)
Bellerive Oval, Hobart 136
19.5%
Sydney Cricket Ground 118
16.9%
Adelaide Oval 114
16.3%
Simonds Stadium, South Geelong, Victoria 108
15.5%
Perth Stadium 85
12.2%
Melbourne Cricket Ground 71
10.2%
Brisbane Cricket Ground, Woolloongabba, Brisbane 67
9.6%

Length

2023-11-03T22:18:26.803042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-03T22:18:26.960011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
cricket 256
11.4%
ground 256
11.4%
oval 250
11.1%
stadium 193
 
8.6%
bellerive 136
 
6.0%
hobart 136
 
6.0%
brisbane 134
 
6.0%
sydney 118
 
5.2%
adelaide 114
 
5.1%
simonds 108
 
4.8%
Other values (6) 547
24.3%

Most occurring characters

ValueCountFrequency (%)
e 1587
 
9.2%
1549
 
9.0%
r 1182
 
6.8%
o 1163
 
6.7%
i 1157
 
6.7%
a 1069
 
6.2%
l 949
 
5.5%
d 903
 
5.2%
t 886
 
5.1%
n 862
 
5.0%
Other values (22) 5990
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13014
75.2%
Uppercase Letter 2248
 
13.0%
Space Separator 1549
 
9.0%
Other Punctuation 486
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1587
12.2%
r 1182
9.1%
o 1163
8.9%
i 1157
8.9%
a 1069
8.2%
l 949
 
7.3%
d 903
 
6.9%
t 886
 
6.8%
n 862
 
6.6%
u 628
 
4.8%
Other values (9) 2628
20.2%
Uppercase Letter
ValueCountFrequency (%)
S 527
23.4%
G 364
16.2%
B 270
12.0%
C 256
11.4%
O 250
11.1%
H 136
 
6.0%
A 114
 
5.1%
V 108
 
4.8%
P 85
 
3.8%
M 71
 
3.2%
Space Separator
ValueCountFrequency (%)
1549
100.0%
Other Punctuation
ValueCountFrequency (%)
, 486
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15262
88.2%
Common 2035
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1587
 
10.4%
r 1182
 
7.7%
o 1163
 
7.6%
i 1157
 
7.6%
a 1069
 
7.0%
l 949
 
6.2%
d 903
 
5.9%
t 886
 
5.8%
n 862
 
5.6%
u 628
 
4.1%
Other values (20) 4876
31.9%
Common
ValueCountFrequency (%)
1549
76.1%
, 486
 
23.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1587
 
9.2%
1549
 
9.0%
r 1182
 
6.8%
o 1163
 
6.7%
i 1157
 
6.7%
a 1069
 
6.2%
l 949
 
5.5%
d 903
 
5.2%
t 886
 
5.1%
n 862
 
5.0%
Other values (22) 5990
34.6%

city
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Hobart
136 
Sydney
118 
Adelaide
114 
Geelong
108 
Perth
85 
Other values (2)
138 

Length

Max length9
Median length8
Mean length6.8555079
Min length5

Characters and Unicode

Total characters4792
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMelbourne
2nd rowMelbourne
3rd rowMelbourne
4th rowMelbourne
5th rowMelbourne

Common Values

ValueCountFrequency (%)
Hobart 136
19.5%
Sydney 118
16.9%
Adelaide 114
16.3%
Geelong 108
15.5%
Perth 85
12.2%
Melbourne 71
10.2%
Brisbane 67
9.6%

Length

2023-11-03T22:18:27.130161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-03T22:18:27.303051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
hobart 136
19.5%
sydney 118
16.9%
adelaide 114
16.3%
geelong 108
15.5%
perth 85
12.2%
melbourne 71
10.2%
brisbane 67
9.6%

Most occurring characters

ValueCountFrequency (%)
e 856
17.9%
n 364
 
7.6%
r 359
 
7.5%
d 346
 
7.2%
a 317
 
6.6%
o 315
 
6.6%
l 293
 
6.1%
b 274
 
5.7%
y 236
 
4.9%
t 221
 
4.6%
Other values (12) 1211
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4093
85.4%
Uppercase Letter 699
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 856
20.9%
n 364
8.9%
r 359
8.8%
d 346
8.5%
a 317
 
7.7%
o 315
 
7.7%
l 293
 
7.2%
b 274
 
6.7%
y 236
 
5.8%
t 221
 
5.4%
Other values (5) 512
12.5%
Uppercase Letter
ValueCountFrequency (%)
H 136
19.5%
S 118
16.9%
A 114
16.3%
G 108
15.5%
P 85
12.2%
M 71
10.2%
B 67
9.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 4792
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 856
17.9%
n 364
 
7.6%
r 359
 
7.5%
d 346
 
7.2%
a 317
 
6.6%
o 315
 
6.6%
l 293
 
6.1%
b 274
 
5.7%
y 236
 
4.9%
t 221
 
4.6%
Other values (12) 1211
25.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 856
17.9%
n 364
 
7.6%
r 359
 
7.5%
d 346
 
7.2%
a 317
 
6.6%
o 315
 
6.6%
l 293
 
6.1%
b 274
 
5.7%
y 236
 
4.9%
t 221
 
4.6%
Other values (12) 1211
25.3%

country
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Australia
699 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters6291
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAustralia
2nd rowAustralia
3rd rowAustralia
4th rowAustralia
5th rowAustralia

Common Values

ValueCountFrequency (%)
Australia 699
100.0%

Length

2023-11-03T22:18:27.444369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-03T22:18:27.614591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
australia 699
100.0%

Most occurring characters

ValueCountFrequency (%)
a 1398
22.2%
A 699
11.1%
u 699
11.1%
s 699
11.1%
t 699
11.1%
r 699
11.1%
l 699
11.1%
i 699
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5592
88.9%
Uppercase Letter 699
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1398
25.0%
u 699
12.5%
s 699
12.5%
t 699
12.5%
r 699
12.5%
l 699
12.5%
i 699
12.5%
Uppercase Letter
ValueCountFrequency (%)
A 699
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6291
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1398
22.2%
A 699
11.1%
u 699
11.1%
s 699
11.1%
t 699
11.1%
r 699
11.1%
l 699
11.1%
i 699
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6291
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1398
22.2%
A 699
11.1%
u 699
11.1%
s 699
11.1%
t 699
11.1%
r 699
11.1%
l 699
11.1%
i 699
11.1%

current_innings
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
ZIM
74 
NED
73 
SL
66 
IRE
63 
PAK
57 
Other values (11)
366 

Length

Max length5
Median length3
Mean length2.9213162
Min length2

Characters and Unicode

Total characters2042
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPAK
2nd rowPAK
3rd rowPAK
4th rowPAK
5th rowPAK

Common Values

ValueCountFrequency (%)
ZIM 74
10.6%
NED 73
10.4%
SL 66
9.4%
IRE 63
 
9.0%
PAK 57
 
8.2%
BAN 47
 
6.7%
INDIA 45
 
6.4%
ENG 39
 
5.6%
SA 37
 
5.3%
NZ 36
 
5.2%
Other values (6) 162
23.2%

Length

2023-11-03T22:18:27.726544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
zim 74
10.6%
ned 73
10.4%
sl 66
9.4%
ire 63
 
9.0%
pak 57
 
8.2%
ban 47
 
6.7%
india 45
 
6.4%
eng 39
 
5.6%
sa 37
 
5.3%
nz 36
 
5.2%
Other values (6) 162
23.2%

Most occurring characters

ValueCountFrequency (%)
A 300
14.7%
N 266
13.0%
I 254
12.4%
E 201
9.8%
S 157
 
7.7%
D 118
 
5.8%
Z 110
 
5.4%
M 100
 
4.9%
G 68
 
3.3%
L 66
 
3.2%
Other values (10) 402
19.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2042
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 300
14.7%
N 266
13.0%
I 254
12.4%
E 201
9.8%
S 157
 
7.7%
D 118
 
5.8%
Z 110
 
5.4%
M 100
 
4.9%
G 68
 
3.3%
L 66
 
3.2%
Other values (10) 402
19.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 2042
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 300
14.7%
N 266
13.0%
I 254
12.4%
E 201
9.8%
S 157
 
7.7%
D 118
 
5.8%
Z 110
 
5.4%
M 100
 
4.9%
G 68
 
3.3%
L 66
 
3.2%
Other values (10) 402
19.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2042
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 300
14.7%
N 266
13.0%
I 254
12.4%
E 201
9.8%
S 157
 
7.7%
D 118
 
5.8%
Z 110
 
5.4%
M 100
 
4.9%
G 68
 
3.3%
L 66
 
3.2%
Other values (10) 402
19.7%

innings_id
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1
351 
2
348 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters699
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 351
50.2%
2 348
49.8%

Length

2023-11-03T22:18:27.852223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-03T22:18:27.994265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 351
50.2%
2 348
49.8%

Most occurring characters

ValueCountFrequency (%)
1 351
50.2%
2 348
49.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 699
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 351
50.2%
2 348
49.8%

Most occurring scripts

ValueCountFrequency (%)
Common 699
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 351
50.2%
2 348
49.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 351
50.2%
2 348
49.8%

name
Text

Distinct198
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2023-11-03T22:18:28.540067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length21
Median length18
Mean length10.676681
Min length6

Characters and Unicode

Total characters7463
Distinct characters58
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)5.6%

Sample

1st rowMohammad Rizwan
2nd rowBabar Azam
3rd rowMohammad Haris
4th rowShan Masood
5th rowIftikhar Ahmed
ValueCountFrequency (%)
de 27
 
1.8%
mohammad 22
 
1.5%
van 16
 
1.1%
hossain 15
 
1.0%
silva 14
 
1.0%
sa 14
 
1.0%
hasan 13
 
0.9%
m 13
 
0.9%
r 13
 
0.9%
c 13
 
0.9%
Other values (332) 1305
89.1%
2023-11-03T22:18:29.402975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 893
 
12.0%
766
 
10.3%
e 398
 
5.3%
i 368
 
4.9%
n 362
 
4.9%
r 336
 
4.5%
s 267
 
3.6%
h 266
 
3.6%
l 255
 
3.4%
d 233
 
3.1%
Other values (48) 3319
44.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4752
63.7%
Uppercase Letter 1908
25.6%
Space Separator 766
 
10.3%
Close Punctuation 8
 
0.1%
Open Punctuation 8
 
0.1%
Other Punctuation 8
 
0.1%
Decimal Number 8
 
0.1%
Dash Punctuation 5
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 893
18.8%
e 398
 
8.4%
i 368
 
7.7%
n 362
 
7.6%
r 336
 
7.1%
s 267
 
5.6%
h 266
 
5.6%
l 255
 
5.4%
d 233
 
4.9%
o 226
 
4.8%
Other values (16) 1148
24.2%
Uppercase Letter
ValueCountFrequency (%)
M 208
 
10.9%
S 195
 
10.2%
A 159
 
8.3%
R 138
 
7.2%
C 117
 
6.1%
B 107
 
5.6%
H 106
 
5.6%
D 101
 
5.3%
J 100
 
5.2%
P 100
 
5.2%
Other values (15) 577
30.2%
Decimal Number
ValueCountFrequency (%)
3 5
62.5%
1 3
37.5%
Space Separator
ValueCountFrequency (%)
766
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Other Punctuation
ValueCountFrequency (%)
' 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6660
89.2%
Common 803
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 893
 
13.4%
e 398
 
6.0%
i 368
 
5.5%
n 362
 
5.4%
r 336
 
5.0%
s 267
 
4.0%
h 266
 
4.0%
l 255
 
3.8%
d 233
 
3.5%
o 226
 
3.4%
Other values (41) 3056
45.9%
Common
ValueCountFrequency (%)
766
95.4%
) 8
 
1.0%
( 8
 
1.0%
' 8
 
1.0%
3 5
 
0.6%
- 5
 
0.6%
1 3
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 893
 
12.0%
766
 
10.3%
e 398
 
5.3%
i 368
 
4.9%
n 362
 
4.9%
r 336
 
4.5%
s 267
 
3.6%
h 266
 
3.6%
l 255
 
3.4%
d 233
 
3.1%
Other values (48) 3319
44.5%
Distinct198
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2023-11-03T22:18:29.919845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length26
Median length20
Mean length13.416309
Min length7

Characters and Unicode

Total characters9378
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)5.6%

Sample

1st rowMohammad Rizwan
2nd rowBabar Azam
3rd rowMohammad Haris
4th rowShan Masood
5th rowIftikhar Ahmed
ValueCountFrequency (%)
de 27
 
1.8%
mohammad 22
 
1.5%
van 16
 
1.1%
hossain 15
 
1.0%
silva 14
 
0.9%
hasan 13
 
0.9%
ahmed 12
 
0.8%
khan 12
 
0.8%
mitchell 12
 
0.8%
david 12
 
0.8%
Other values (347) 1319
89.5%
2023-11-03T22:18:30.609239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1292
 
13.8%
775
 
8.3%
n 614
 
6.5%
e 597
 
6.4%
i 577
 
6.2%
r 505
 
5.4%
h 416
 
4.4%
s 387
 
4.1%
l 375
 
4.0%
o 350
 
3.7%
Other values (44) 3490
37.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7136
76.1%
Uppercase Letter 1454
 
15.5%
Space Separator 775
 
8.3%
Other Punctuation 8
 
0.1%
Dash Punctuation 5
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1292
18.1%
n 614
 
8.6%
e 597
 
8.4%
i 577
 
8.1%
r 505
 
7.1%
h 416
 
5.8%
s 387
 
5.4%
l 375
 
5.3%
o 350
 
4.9%
d 301
 
4.2%
Other values (16) 1722
24.1%
Uppercase Letter
ValueCountFrequency (%)
M 177
12.2%
S 168
11.6%
A 118
 
8.1%
R 100
 
6.9%
C 95
 
6.5%
B 87
 
6.0%
H 81
 
5.6%
D 76
 
5.2%
K 73
 
5.0%
L 66
 
4.5%
Other values (15) 413
28.4%
Space Separator
ValueCountFrequency (%)
775
100.0%
Other Punctuation
ValueCountFrequency (%)
' 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8590
91.6%
Common 788
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1292
15.0%
n 614
 
7.1%
e 597
 
6.9%
i 577
 
6.7%
r 505
 
5.9%
h 416
 
4.8%
s 387
 
4.5%
l 375
 
4.4%
o 350
 
4.1%
d 301
 
3.5%
Other values (41) 3176
37.0%
Common
ValueCountFrequency (%)
775
98.4%
' 8
 
1.0%
- 5
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1292
 
13.8%
775
 
8.3%
n 614
 
6.5%
e 597
 
6.4%
i 577
 
6.2%
r 505
 
5.4%
h 416
 
4.4%
s 387
 
4.1%
l 375
 
4.0%
o 350
 
3.7%
Other values (44) 3490
37.2%

runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.978541
Minimum0
Maximum109
Zeros82
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-11-03T22:18:30.780921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q322.5
95-th percentile59
Maximum109
Range109
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation18.709462
Coefficient of variation (CV)1.1709118
Kurtosis3.1761447
Mean15.978541
Median Absolute Deviation (MAD)8
Skewness1.760889
Sum11169
Variance350.04395
MonotonicityNot monotonic
2023-11-03T22:18:30.907983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 82
 
11.7%
1 60
 
8.6%
4 38
 
5.4%
2 35
 
5.0%
3 28
 
4.0%
5 28
 
4.0%
6 27
 
3.9%
7 23
 
3.3%
9 21
 
3.0%
8 21
 
3.0%
Other values (68) 336
48.1%
ValueCountFrequency (%)
0 82
11.7%
1 60
8.6%
2 35
5.0%
3 28
 
4.0%
4 38
5.4%
5 28
 
4.0%
6 27
 
3.9%
7 23
 
3.3%
8 21
 
3.0%
9 21
 
3.0%
ValueCountFrequency (%)
109 1
0.1%
104 1
0.1%
92 1
0.1%
86 2
0.3%
82 2
0.3%
80 1
0.1%
79 1
0.1%
74 1
0.1%
73 1
0.1%
72 1
0.1%

ballsFaced
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.648069
Minimum0
Maximum64
Zeros11
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-11-03T22:18:31.081080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median10
Q319
95-th percentile42
Maximum64
Range64
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.716854
Coefficient of variation (CV)0.93176949
Kurtosis1.5262267
Mean13.648069
Median Absolute Deviation (MAD)7
Skewness1.3939557
Sum9540
Variance161.71837
MonotonicityNot monotonic
2023-11-03T22:18:31.222534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 53
 
7.6%
2 45
 
6.4%
3 43
 
6.2%
6 41
 
5.9%
5 38
 
5.4%
4 36
 
5.2%
8 34
 
4.9%
7 27
 
3.9%
11 26
 
3.7%
18 25
 
3.6%
Other values (48) 331
47.4%
ValueCountFrequency (%)
0 11
 
1.6%
1 53
7.6%
2 45
6.4%
3 43
6.2%
4 36
5.2%
5 38
5.4%
6 41
5.9%
7 27
3.9%
8 34
4.9%
9 21
 
3.0%
ValueCountFrequency (%)
64 1
 
0.1%
60 1
 
0.1%
58 1
 
0.1%
56 1
 
0.1%
55 2
 
0.3%
54 1
 
0.1%
53 3
0.4%
51 1
 
0.1%
49 2
 
0.3%
48 7
1.0%
Distinct90
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2023-11-03T22:18:31.628235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length2
Mean length1.6824034
Min length1

Characters and Unicode

Total characters1176
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)2.9%

Sample

1st row24
2nd row58
3rd row15
4th row46
5th row8
ValueCountFrequency (%)
3 37
 
5.3%
4 33
 
4.7%
8 29
 
4.1%
6 28
 
4.0%
9 27
 
3.9%
12 24
 
3.4%
10 23
 
3.3%
2 23
 
3.3%
11 22
 
3.1%
7 22
 
3.1%
Other values (80) 431
61.7%
2023-11-03T22:18:32.254708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 245
20.8%
2 183
15.6%
3 152
12.9%
4 121
10.3%
6 89
 
7.6%
7 88
 
7.5%
5 83
 
7.1%
8 80
 
6.8%
0 68
 
5.8%
9 66
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1175
99.9%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 245
20.9%
2 183
15.6%
3 152
12.9%
4 121
10.3%
6 89
 
7.6%
7 88
 
7.5%
5 83
 
7.1%
8 80
 
6.8%
0 68
 
5.8%
9 66
 
5.6%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 245
20.8%
2 183
15.6%
3 152
12.9%
4 121
10.3%
6 89
 
7.6%
7 88
 
7.5%
5 83
 
7.1%
8 80
 
6.8%
0 68
 
5.8%
9 66
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 245
20.8%
2 183
15.6%
3 152
12.9%
4 121
10.3%
6 89
 
7.6%
7 88
 
7.5%
5 83
 
7.1%
8 80
 
6.8%
0 68
 
5.8%
9 66
 
5.6%

fours
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3004292
Minimum0
Maximum10
Zeros338
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-11-03T22:18:32.427055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5.1
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8344483
Coefficient of variation (CV)1.4106483
Kurtosis3.48308
Mean1.3004292
Median Absolute Deviation (MAD)1
Skewness1.8502902
Sum909
Variance3.3652004
MonotonicityNot monotonic
2023-11-03T22:18:32.552718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 338
48.4%
1 141
20.2%
2 83
 
11.9%
3 66
 
9.4%
4 21
 
3.0%
5 15
 
2.1%
7 14
 
2.0%
6 13
 
1.9%
8 4
 
0.6%
9 3
 
0.4%
ValueCountFrequency (%)
0 338
48.4%
1 141
20.2%
2 83
 
11.9%
3 66
 
9.4%
4 21
 
3.0%
5 15
 
2.1%
6 13
 
1.9%
7 14
 
2.0%
8 4
 
0.6%
9 3
 
0.4%
ValueCountFrequency (%)
10 1
 
0.1%
9 3
 
0.4%
8 4
 
0.6%
7 14
 
2.0%
6 13
 
1.9%
5 15
 
2.1%
4 21
 
3.0%
3 66
9.4%
2 83
11.9%
1 141
20.2%

sixes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47353362
Minimum0
Maximum8
Zeros502
Zeros (%)71.8%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-11-03T22:18:32.666497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.97846466
Coefficient of variation (CV)2.0663045
Kurtosis12.575237
Mean0.47353362
Median Absolute Deviation (MAD)0
Skewness3.0697125
Sum331
Variance0.95739308
MonotonicityNot monotonic
2023-11-03T22:18:32.757487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 502
71.8%
1 125
 
17.9%
2 38
 
5.4%
3 19
 
2.7%
4 8
 
1.1%
5 4
 
0.6%
7 1
 
0.1%
8 1
 
0.1%
6 1
 
0.1%
ValueCountFrequency (%)
0 502
71.8%
1 125
 
17.9%
2 38
 
5.4%
3 19
 
2.7%
4 8
 
1.1%
5 4
 
0.6%
6 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
7 1
 
0.1%
6 1
 
0.1%
5 4
 
0.6%
4 8
 
1.1%
3 19
 
2.7%
2 38
 
5.4%
1 125
 
17.9%
0 502
71.8%
Distinct240
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2023-11-03T22:18:33.352235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.3705293
Min length1

Characters and Unicode

Total characters3754
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique158 ?
Unique (%)22.6%

Sample

1st row107.14
2nd row114.28
3rd row66.66
4th row135.71
5th row0.00
ValueCountFrequency (%)
0.00 71
 
10.2%
100.00 69
 
9.9%
50.00 35
 
5.0%
66.66 18
 
2.6%
33.33 15
 
2.1%
133.33 14
 
2.0%
75.00 14
 
2.0%
200.00 12
 
1.7%
150.00 12
 
1.7%
11
 
1.6%
Other values (230) 428
61.2%
2023-11-03T22:18:34.145200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1076
28.7%
. 688
18.3%
1 478
12.7%
3 267
 
7.1%
6 255
 
6.8%
5 239
 
6.4%
2 210
 
5.6%
8 162
 
4.3%
7 151
 
4.0%
4 147
 
3.9%
Other values (2) 81
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3055
81.4%
Other Punctuation 688
 
18.3%
Dash Punctuation 11
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1076
35.2%
1 478
15.6%
3 267
 
8.7%
6 255
 
8.3%
5 239
 
7.8%
2 210
 
6.9%
8 162
 
5.3%
7 151
 
4.9%
4 147
 
4.8%
9 70
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 688
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1076
28.7%
. 688
18.3%
1 478
12.7%
3 267
 
7.1%
6 255
 
6.8%
5 239
 
6.4%
2 210
 
5.6%
8 162
 
4.3%
7 151
 
4.0%
4 147
 
3.9%
Other values (2) 81
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1076
28.7%
. 688
18.3%
1 478
12.7%
3 267
 
7.1%
6 255
 
6.8%
5 239
 
6.4%
2 210
 
5.6%
8 162
 
4.3%
7 151
 
4.0%
4 147
 
3.9%
Other values (2) 81
 
2.2%

captain
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size831.0 B
False
616 
True
83 
ValueCountFrequency (%)
False 616
88.1%
True 83
 
11.9%
2023-11-03T22:18:34.345825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

isNotOut
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size831.0 B
False
555 
True
144 
ValueCountFrequency (%)
False 555
79.4%
True 144
 
20.6%
2023-11-03T22:18:34.486723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct426
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2023-11-03T22:18:34.862792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length28
Median length27
Mean length21.316166
Min length2

Characters and Unicode

Total characters14900
Distinct characters26
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique328 ?
Unique (%)46.9%

Sample

1st row{'wickets': 1, 'runs': 29}
2nd row{'wickets': 3, 'runs': 84}
3rd row{'wickets': 2, 'runs': 45}
4th row{'wickets': 5, 'runs': 121}
5th row{'wickets': 4, 'runs': 85}
ValueCountFrequency (%)
runs 555
23.5%
wickets 555
23.5%
144
 
6.1%
1 86
 
3.6%
2 84
 
3.6%
4 81
 
3.4%
3 81
 
3.4%
5 73
 
3.1%
6 58
 
2.5%
7 48
 
2.0%
Other values (161) 599
25.3%
2023-11-03T22:18:35.452141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 2220
14.9%
1665
 
11.2%
s 1110
 
7.4%
: 1110
 
7.4%
{ 699
 
4.7%
} 699
 
4.7%
n 555
 
3.7%
u 555
 
3.7%
r 555
 
3.7%
, 555
 
3.7%
Other values (16) 5177
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6105
41.0%
Other Punctuation 3885
26.1%
Decimal Number 1847
 
12.4%
Space Separator 1665
 
11.2%
Open Punctuation 699
 
4.7%
Close Punctuation 699
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 1110
18.2%
n 555
9.1%
u 555
9.1%
r 555
9.1%
t 555
9.1%
e 555
9.1%
k 555
9.1%
c 555
9.1%
i 555
9.1%
w 555
9.1%
Decimal Number
ValueCountFrequency (%)
1 443
24.0%
2 217
11.7%
3 177
 
9.6%
6 165
 
8.9%
4 164
 
8.9%
5 160
 
8.7%
7 141
 
7.6%
9 139
 
7.5%
0 128
 
6.9%
8 113
 
6.1%
Other Punctuation
ValueCountFrequency (%)
' 2220
57.1%
: 1110
28.6%
, 555
 
14.3%
Space Separator
ValueCountFrequency (%)
1665
100.0%
Open Punctuation
ValueCountFrequency (%)
{ 699
100.0%
Close Punctuation
ValueCountFrequency (%)
} 699
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8795
59.0%
Latin 6105
41.0%

Most frequent character per script

Common
ValueCountFrequency (%)
' 2220
25.2%
1665
18.9%
: 1110
12.6%
{ 699
 
7.9%
} 699
 
7.9%
, 555
 
6.3%
1 443
 
5.0%
2 217
 
2.5%
3 177
 
2.0%
6 165
 
1.9%
Other values (6) 845
 
9.6%
Latin
ValueCountFrequency (%)
s 1110
18.2%
n 555
9.1%
u 555
9.1%
r 555
9.1%
t 555
9.1%
e 555
9.1%
k 555
9.1%
c 555
9.1%
i 555
9.1%
w 555
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 2220
14.9%
1665
 
11.2%
s 1110
 
7.4%
: 1110
 
7.4%
{ 699
 
4.7%
} 699
 
4.7%
n 555
 
3.7%
u 555
 
3.7%
r 555
 
3.7%
, 555
 
3.7%
Other values (16) 5177
34.7%

runningOver
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct119
Distinct (%)21.4%
Missing144
Missing (%)20.6%
Infinite0
Infinite (%)0.0%
Mean11.198378
Minimum0.1
Maximum19.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-11-03T22:18:35.607421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.4
Q15.6
median12.3
Q316.35
95-th percentile19.33
Maximum19.6
Range19.5
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation5.9454968
Coefficient of variation (CV)0.5309248
Kurtosis-1.2204451
Mean11.198378
Median Absolute Deviation (MAD)5
Skewness-0.29101911
Sum6215.1
Variance35.348932
MonotonicityNot monotonic
2023-11-03T22:18:35.751767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.3 11
 
1.6%
19.6 11
 
1.6%
12.2 10
 
1.4%
2.4 10
 
1.4%
19.5 10
 
1.4%
19.2 10
 
1.4%
13.3 9
 
1.3%
18.2 9
 
1.3%
3.2 8
 
1.1%
12.4 8
 
1.1%
Other values (109) 459
65.7%
(Missing) 144
 
20.6%
ValueCountFrequency (%)
0.1 2
 
0.3%
0.2 4
0.6%
0.3 3
0.4%
0.4 2
 
0.3%
0.5 1
 
0.1%
0.6 4
0.6%
1.1 5
0.7%
1.2 2
 
0.3%
1.3 4
0.6%
1.4 5
0.7%
ValueCountFrequency (%)
19.6 11
1.6%
19.5 10
1.4%
19.4 7
1.0%
19.3 6
0.9%
19.2 10
1.4%
19.1 3
 
0.4%
19 1
 
0.1%
18.6 6
0.9%
18.5 4
 
0.6%
18.4 7
1.0%
Distinct435
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2023-11-03T22:18:36.253091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length51
Median length41
Mean length18.423462
Min length7

Characters and Unicode

Total characters12878
Distinct characters60
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique350 ?
Unique (%)50.1%

Sample

1st row b Curran
2nd rowc & b Rashid
3rd rowc Stokes b Rashid
4th rowc Livingstone b Curran
5th rowc †Buttler b Stokes
ValueCountFrequency (%)
b 515
19.5%
c 372
 
14.1%
out 183
 
6.9%
not 144
 
5.5%
de 43
 
1.6%
run 39
 
1.5%
lbw 35
 
1.3%
van 31
 
1.2%
silva 26
 
1.0%
khan 25
 
0.9%
Other values (260) 1222
46.4%
2023-11-03T22:18:36.744034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2033
15.8%
a 1325
 
10.3%
e 733
 
5.7%
n 663
 
5.1%
b 644
 
5.0%
o 636
 
4.9%
r 563
 
4.4%
t 545
 
4.2%
d 467
 
3.6%
u 454
 
3.5%
Other values (50) 4815
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9174
71.2%
Space Separator 2033
 
15.8%
Uppercase Letter 1319
 
10.2%
Other Punctuation 241
 
1.9%
Open Punctuation 50
 
0.4%
Close Punctuation 50
 
0.4%
Dash Punctuation 11
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1325
14.4%
e 733
 
8.0%
n 663
 
7.2%
b 644
 
7.0%
o 636
 
6.9%
r 563
 
6.1%
t 545
 
5.9%
d 467
 
5.1%
u 454
 
4.9%
i 431
 
4.7%
Other values (16) 2713
29.6%
Uppercase Letter
ValueCountFrequency (%)
S 217
16.5%
M 166
12.6%
A 104
 
7.9%
H 95
 
7.2%
R 81
 
6.1%
W 81
 
6.1%
K 75
 
5.7%
C 67
 
5.1%
N 65
 
4.9%
L 59
 
4.5%
Other values (14) 309
23.4%
Other Punctuation
ValueCountFrequency (%)
; 107
44.4%
& 107
44.4%
/ 24
 
10.0%
' 3
 
1.2%
Open Punctuation
ValueCountFrequency (%)
( 49
98.0%
[ 1
 
2.0%
Close Punctuation
ValueCountFrequency (%)
) 49
98.0%
] 1
 
2.0%
Space Separator
ValueCountFrequency (%)
2033
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10493
81.5%
Common 2385
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1325
 
12.6%
e 733
 
7.0%
n 663
 
6.3%
b 644
 
6.1%
o 636
 
6.1%
r 563
 
5.4%
t 545
 
5.2%
d 467
 
4.5%
u 454
 
4.3%
i 431
 
4.1%
Other values (40) 4032
38.4%
Common
ValueCountFrequency (%)
2033
85.2%
; 107
 
4.5%
& 107
 
4.5%
( 49
 
2.1%
) 49
 
2.1%
/ 24
 
1.0%
- 11
 
0.5%
' 3
 
0.1%
[ 1
 
< 0.1%
] 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12878
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2033
15.8%
a 1325
 
10.3%
e 733
 
5.7%
n 663
 
5.1%
b 644
 
5.0%
o 636
 
4.9%
r 563
 
4.4%
t 545
 
4.2%
d 467
 
3.6%
u 454
 
3.5%
Other values (50) 4815
37.4%

commentary
Text

MISSING 

Distinct555
Distinct (%)100.0%
Missing144
Missing (%)20.6%
Memory size5.6 KiB
2023-11-03T22:18:37.141714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length782
Median length352
Mean length277.09369
Min length67

Characters and Unicode

Total characters153787
Distinct characters79
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique555 ?
Unique (%)100.0%

Sample

1st rowbowled him, Rizwan's dragged on! Fullish, slanting into a wide-ish line, inviting the cover drive. Nips back in off the pitch, and Rizwan, going hard with a diagonal bat, can only get his inside edge to it
2nd row<B>caught and bowled!</B> That's the vital scalp, and Rashid has bagged it! it's another googly, another big wind-up from Babar, looking to take it on in the second half of the innings, but the ball gripped and turned back into his attempted cut/slash through the off-side, and looped back to the bowler off the splice! Huge turning point
3rd rowand he strikes first ball! #MCGsobig strikes again. Tossed up fairly slow, and Haris steps out and tries to take on Stokes, who seems to be stationed a good few yards inside the rope at long-on. Can't really middle it, and Stokes moves in a little more to take a straightforward catch
4th row<B>flicked in the air to Livingstone at midwicket!</B> Don't play roulette with Sam Curran! Masood shimmied to leg, tempting Curran to reconsider his options, he simply bowled his best ball, shaping away on a tight off-stump line, and the lofted cross-bat clip didn't have enough on it. A simple take in the deep, and Dr Death has done the trick once more...
5th row<B>nibbles outside off, thin edge to Buttler!</B> Line and length from Stokes, extra lift off the seam, a thin but audible nick and that's a huge five minutes for the balance of play! More evidence, though, that this pitch is offering plenty to the seamers... and Pakistan have plenty of those!
ValueCountFrequency (%)
the 1728
 
6.3%
to 1031
 
3.8%
and 983
 
3.6%
a 782
 
2.8%
it 570
 
2.1%
on 428
 
1.6%
but 409
 
1.5%
off 374
 
1.4%
in 353
 
1.3%
ball 352
 
1.3%
Other values (3525) 20454
74.5%
2023-11-03T22:18:38.750633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26910
17.5%
e 12310
 
8.0%
t 11962
 
7.8%
a 9538
 
6.2%
o 9123
 
5.9%
s 7962
 
5.2%
i 7734
 
5.0%
n 7700
 
5.0%
h 6916
 
4.5%
r 5949
 
3.9%
Other values (69) 47683
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 116664
75.9%
Space Separator 26910
 
17.5%
Other Punctuation 4401
 
2.9%
Uppercase Letter 3258
 
2.1%
Math Symbol 1890
 
1.2%
Dash Punctuation 386
 
0.3%
Decimal Number 276
 
0.2%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12310
 
10.6%
t 11962
 
10.3%
a 9538
 
8.2%
o 9123
 
7.8%
s 7962
 
6.8%
i 7734
 
6.6%
n 7700
 
6.6%
h 6916
 
5.9%
r 5949
 
5.1%
l 5586
 
4.8%
Other values (17) 31884
27.3%
Uppercase Letter
ValueCountFrequency (%)
B 340
 
10.4%
S 334
 
10.3%
T 311
 
9.5%
A 246
 
7.6%
H 192
 
5.9%
M 182
 
5.6%
C 171
 
5.2%
W 169
 
5.2%
I 153
 
4.7%
N 147
 
4.5%
Other values (15) 1013
31.1%
Other Punctuation
ValueCountFrequency (%)
. 1488
33.8%
, 1378
31.3%
! 506
 
11.5%
/ 490
 
11.1%
' 483
 
11.0%
? 40
 
0.9%
: 4
 
0.1%
" 4
 
0.1%
# 4
 
0.1%
; 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 75
27.2%
0 46
16.7%
4 32
11.6%
2 30
 
10.9%
5 23
 
8.3%
3 23
 
8.3%
8 15
 
5.4%
9 12
 
4.3%
6 12
 
4.3%
7 8
 
2.9%
Math Symbol
ValueCountFrequency (%)
> 945
50.0%
< 945
50.0%
Space Separator
ValueCountFrequency (%)
26910
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 386
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119922
78.0%
Common 33865
 
22.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12310
 
10.3%
t 11962
 
10.0%
a 9538
 
8.0%
o 9123
 
7.6%
s 7962
 
6.6%
i 7734
 
6.4%
n 7700
 
6.4%
h 6916
 
5.8%
r 5949
 
5.0%
l 5586
 
4.7%
Other values (42) 35142
29.3%
Common
ValueCountFrequency (%)
26910
79.5%
. 1488
 
4.4%
, 1378
 
4.1%
> 945
 
2.8%
< 945
 
2.8%
! 506
 
1.5%
/ 490
 
1.4%
' 483
 
1.4%
- 386
 
1.1%
1 75
 
0.2%
Other values (17) 259
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 153786
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26910
17.5%
e 12310
 
8.0%
t 11962
 
7.8%
a 9538
 
6.2%
o 9123
 
5.9%
s 7962
 
5.2%
i 7734
 
5.0%
n 7700
 
5.0%
h 6916
 
4.5%
r 5949
 
3.9%
Other values (68) 47682
31.0%
None
ValueCountFrequency (%)
à 1
100.0%

link
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing699
Missing (%)100.0%
Memory size5.6 KiB

Interactions

2023-11-03T22:18:23.894357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:19.306202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:20.206887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:21.222029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:22.132482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:22.980508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:24.014895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:19.495200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:20.301136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:21.370973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:22.258378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:23.160520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:24.153358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:19.639475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:20.464712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:21.497262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:22.374360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:23.334387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:24.302598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:19.789353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:20.663879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:21.651841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:22.505051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:23.447249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:24.482095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:19.930185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:20.862028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:21.798382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:22.630511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:23.561296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:24.628963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:20.053079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:21.063745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:21.931364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:22.814951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-03T22:18:23.730626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-11-03T22:18:38.900016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
match_idrunsballsFacedfourssixesrunningOvermatch_namehome_teamaway_teamvenuecitycurrent_inningsinnings_idcaptainisNotOut
match_id1.0000.0360.0110.0670.0550.0770.9770.5880.5490.5900.5900.3380.0000.0000.000
runs0.0361.0000.9350.8210.6390.1350.0660.0000.0280.0380.0380.0370.0000.0000.143
ballsFaced0.0110.9351.0000.7330.5440.0930.0590.0000.0450.0000.0000.0720.0000.0780.121
fours0.0670.8210.7331.0000.4330.0350.0930.0000.0000.0320.0320.0090.0000.0000.152
sixes0.0550.6390.5440.4331.0000.0840.1030.0510.0800.0000.0000.0550.0760.0660.115
runningOver0.0770.1350.0930.0350.0841.0000.0000.0000.0000.0260.0260.0000.2290.0591.000
match_name0.9770.0660.0590.0930.1030.0001.0000.9810.9800.9740.9740.6610.0000.0000.000
home_team0.5880.0000.0000.0000.0510.0000.9811.0000.6100.6870.6870.5580.0000.0000.000
away_team0.5490.0280.0450.0000.0800.0000.9800.6101.0000.5890.5890.5370.0000.0000.000
venue0.5900.0380.0000.0320.0000.0260.9740.6870.5891.0001.0000.4920.0550.0000.000
city0.5900.0380.0000.0320.0000.0260.9740.6870.5891.0001.0000.4920.0550.0000.000
current_innings0.3380.0370.0720.0090.0550.0000.6610.5580.5370.4920.4921.0000.3800.0000.000
innings_id0.0000.0000.0000.0000.0760.2290.0000.0000.0000.0550.0550.3801.0000.0000.000
captain0.0000.0000.0780.0000.0660.0590.0000.0000.0000.0000.0000.0000.0001.0000.098
isNotOut0.0000.1430.1210.1520.1151.0000.0000.0000.0000.0000.0000.0000.0000.0981.000

Missing values

2023-11-03T22:18:24.864039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-03T22:18:25.266833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-03T22:18:25.532794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

seasonmatch_idmatch_namehome_teamaway_teamvenuecitycountrycurrent_inningsinnings_idnamefullNamerunsballsFacedminutesfourssixesstrikeRatecaptainisNotOutrunningScorerunningOvershortTextcommentarylink
020221298179ENG v PAKPAKENGMelbourne Cricket GroundMelbourneAustraliaPAK1Mohammad RizwanMohammad Rizwan15142401107.14FalseFalse{'wickets': 1, 'runs': 29}4.2b Curranbowled him, Rizwan's dragged on! Fullish, slanting into a wide-ish line, inviting the cover drive. Nips back in off the pitch, and Rizwan, going hard with a diagonal bat, can only get his inside edge to itNaN
120221298179ENG v PAKPAKENGMelbourne Cricket GroundMelbourneAustraliaPAK1Babar AzamBabar Azam32285820114.28TrueFalse{'wickets': 3, 'runs': 84}11.1c &amp; b Rashid<B>caught and bowled!</B> That's the vital scalp, and Rashid has bagged it! it's another googly, another big wind-up from Babar, looking to take it on in the second half of the innings, but the ball gripped and turned back into his attempted cut/slash through the off-side, and looped back to the bowler off the splice! Huge turning pointNaN
220221298179ENG v PAKPAKENGMelbourne Cricket GroundMelbourneAustraliaPAK1Mohammad HarisMohammad Haris812151066.66FalseFalse{'wickets': 2, 'runs': 45}7.1c Stokes b Rashidand he strikes first ball! #MCGsobig strikes again. Tossed up fairly slow, and Haris steps out and tries to take on Stokes, who seems to be stationed a good few yards inside the rope at long-on. Can't really middle it, and Stokes moves in a little more to take a straightforward catchNaN
320221298179ENG v PAKPAKENGMelbourne Cricket GroundMelbourneAustraliaPAK1Shan MasoodShan Masood38284621135.71FalseFalse{'wickets': 5, 'runs': 121}16.3c Livingstone b Curran<B>flicked in the air to Livingstone at midwicket!</B> Don't play roulette with Sam Curran! Masood shimmied to leg, tempting Curran to reconsider his options, he simply bowled his best ball, shaping away on a tight off-stump line, and the lofted cross-bat clip didn't have enough on it. A simple take in the deep, and Dr Death has done the trick once more...NaN
420221298179ENG v PAKPAKENGMelbourne Cricket GroundMelbourneAustraliaPAK1Iftikhar AhmedIftikhar Ahmed068000.00FalseFalse{'wickets': 4, 'runs': 85}12.2c &dagger;Buttler b Stokes<B>nibbles outside off, thin edge to Buttler!</B> Line and length from Stokes, extra lift off the seam, a thin but audible nick and that's a huge five minutes for the balance of play! More evidence, though, that this pitch is offering plenty to the seamers... and Pakistan have plenty of those!NaN
520221298179ENG v PAKPAKENGMelbourne Cricket GroundMelbourneAustraliaPAK1Shadab KhanShadab Khan20142620142.85FalseFalse{'wickets': 6, 'runs': 123}17.2c Woakes b Jordan<B>another slap across the line, another miscue!</B> Into the pitch once more from Jordan, Shadab tries to impart some oomph through the rising ball, but it's a simple take for Woakes at mid-off, on the edge of the ring. This innings is starting to splutter nowNaN
620221298179ENG v PAKPAKENGMelbourne Cricket GroundMelbourneAustraliaPAK1Mohammad Nawaz(3)Mohammad Nawaz57110071.42FalseFalse{'wickets': 7, 'runs': 129}18.3c Livingstone b Curran<B>fires in the full length, chipped to Livingstone in the deep!</B> Curran is making his play for man of the tournament here! Nawaz thought this was his release ball, tailing into his toes, but with these vast square boundaries, he needed more elevation than that to beat the man in the deep.NaN
720221298179ENG v PAKPAKENGMelbourne Cricket GroundMelbourneAustraliaPAK1Mohammad Wasim(1)Mohammad Wasim48120050.00FalseFalse{'wickets': 8, 'runs': 131}19.3c Livingstone b Jordanslower ball, back of a length, <B>hoisted to Livingstone in the deep once more!</B> Wasim had to take it on, wide long-on was lurking, ready to pounce, he had to make good ground but held on well while falling forward. Pakistan's innings is sliding away nowNaN
820221298179ENG v PAKPAKENGMelbourne Cricket GroundMelbourneAustraliaPAK1Shaheen Shah AfridiShaheen Shah Afridi53810166.66FalseTrue{}NaNnot outNaNNaN
920221298179ENG v PAKPAKENGMelbourne Cricket GroundMelbourneAustraliaPAK1Haris RaufHaris Rauf11300100.00FalseTrue{}NaNnot outNaNNaN
seasonmatch_idmatch_namehome_teamaway_teamvenuecitycountrycurrent_inningsinnings_idnamefullNamerunsballsFacedminutesfourssixesstrikeRatecaptainisNotOutrunningScorerunningOvershortTextcommentarylink
68920221298135NAM v SLNAMSLSimonds Stadium, South Geelong, VictoriaGeelongAustraliaSL2BKG MendisKusal Mendis66800100.00FalseFalse{'wickets': 1, 'runs': 12}1.4c &dagger;Green b Wiese<strong>Straight up and Mendis falls early!</strong> Wiese brings his experience into play and gets his side that crucial wicket. Short of a length, Mendis' eyes light up as he gets about to play the pull. But that hurried onto him, and he could only get a top-edge with the ball ballooning up for the keeper to complete a comfortable catch. Wiese is elated.NaN
69020221298135NAM v SLNAMSLSimonds Stadium, South Geelong, VictoriaGeelongAustraliaSL2DM de SilvaDhananjaya de Silva12112510109.09FalseFalse{'wickets': 4, 'runs': 40}6.3c Shikongo b Frylinck<strong>Soft, soft dismissal and Frylinck now strikes!</strong> Nonchalant length ball on the pads that he could have worked anywhere. Instead, he flicks it straight into the hands of Shikongo, who can do no wrong today, at backward square leg and pouches it. SL in trouble.NaN
69120221298135NAM v SLNAMSLSimonds Stadium, South Geelong, VictoriaGeelongAustraliaSL2MD GunathilakaDanushka Gunathilaka012000.00FalseFalse{'wickets': 3, 'runs': 21}3.3c &dagger;Green b Shikongo<strong>TWO IN TWO!</strong> Upset brewing? On a length tailing just a touch away from Gunathilaka, who opens the face of his blade but can get only a tickle to the keeper. The Namibians are ecstatic and why not. Gunathilaka comes and goes.NaN
69220221298135NAM v SLNAMSLSimonds Stadium, South Geelong, VictoriaGeelongAustraliaSL2PBB RajapaksaBhanuka Rajapaksa2021392095.23FalseFalse{'wickets': 5, 'runs': 74}10.3c la Cock b Scholtz<b>And Bhanuka is holes out at long on!</b> The drinks break has brought about the breakthrough as it so often does, as Bhanuka comes down the track looks to get under this to go high and long. He only gets the height though, as La Cock completes a straightforward take.NaN
69320221298135NAM v SLNAMSLSimonds Stadium, South Geelong, VictoriaGeelongAustraliaSL2MD ShanakaDasun Shanaka29233721126.08TrueFalse{'wickets': 7, 'runs': 88}13.6c &dagger;Green b Frylinck<b>And the captain goes!</b> Is that the final nail in the coffin of Sri Lanka? This was a length ball on and Shanaka looks to go big, but only manages a top edge straight up. The keeper calls for it, and completes an excellent catch diving forward.NaN
69420221298135NAM v SLNAMSLSimonds Stadium, South Geelong, VictoriaGeelongAustraliaSL2PWH de SilvaWanindu Hasaranga de Silva4890050.00FalseFalse{'wickets': 6, 'runs': 80}12.3c Loftie-Eaton b Scholtz<b>Top edge and taken brilliantly at deep midwicket!</b> Another one bites the dust, and this time it's Hasaranga, who looks to slog sweep this over deep midwicket but only gets a high top edge. Loftie-Eaton runs in from the ropes and holds on to an excellent diving take. Sri Lanka folding fast here.NaN
69520221298135NAM v SLNAMSLSimonds Stadium, South Geelong, VictoriaGeelongAustraliaSL2C KarunaratneChamika Karunaratne58150062.50FalseFalse{'wickets': 9, 'runs': 92}15.2c Baard b Smit<b>And now it's surely over!</b> Chamika comes down the track to this back of a length ball and swings wildly. Ends up in a top edge down to long off running in. What a performance this has been from Namibia!NaN
69620221298135NAM v SLNAMSLSimonds Stadium, South Geelong, VictoriaGeelongAustraliaSL2Pramod MadushanPramod Madushan00400-FalseFalse{'wickets': 8, 'runs': 88}14.2run out (van Lingen/&dagger;Green)<b>And is this another one?!</b> This looks very close. They look to drop this and run, but Madushan at the non-striker's end is slow off the blocks. And yes, that means he's a good foot short. Sri Lanka don't look like they're coming back from this.NaN
69720221298135NAM v SLNAMSLSimonds Stadium, South Geelong, VictoriaGeelongAustraliaSL2PVD ChameeraDushmantha Chameera815200053.33FalseFalse{'wickets': 10, 'runs': 108}18.6c Erasmus b Wiese<b>And that's that!</b> Chameera looks to swing this over deep midwicket but only finds the man in the deep. What a win for Namibia, as they secure a <b>55-run win!</b>NaN
69820221298135NAM v SLNAMSLSimonds Stadium, South Geelong, VictoriaGeelongAustraliaSL2M TheekshanaMaheesh Theekshana11111401100.00FalseTrue{}NaNnot outNaNNaN